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Found 12 papers, 4 papers with code
One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AI
Conversational agents have been gaining increasing popularity in recent years.
Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's LLM with Open Source SLMs in Production
Many companies use large language models (LLMs) offered as a service, like OpenAI's GPT-4, to create AI-enabled product experiences.
Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection
In this paper, we present Rule By Example (RBE): a novel exemplar-based contrastive learning approach for learning from logical rules for the task of textual content moderation.
Label Agnostic Pre-training for Zero-shot Text Classification
In addition, depending on the aspect (sentiment, topic, etc.)
The Jaseci Programming Paradigm and Runtime Stack: Building Scale-out Production Applications Easy and Fast
Today's production scale-out applications include many sub-application components, such as storage backends, logging infrastructure and AI models.
The Case for a Wholistic Serverless Programming Paradigm and Full Stack Automation for AI and Beyond -- The Philosophy of Jaseci and Jac
In this work, the case is made for a wholistic top-down re-envisioning of the system stack from the programming language level down through the system architecture to bridge this complexity gap.
One Agent To Rule Them All: Towards Multi-agent Conversational AI
To address these problems, we introduce a new task BBAI: Black-Box Agent Integration, focusing on combining the capabilities of multiple black-box CAs at scale.
Ranked #1 on
Multi-agent Integration
on BBAI Dataset
Conversational Response Selection
Multi-agent Integration
+1
Towards Personalized Intelligence at Scale
Personalized Intelligence (PI) is the problem of providing customized AI experiences tailored to each individual user.
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries.
Outlier Detection for Improved Data Quality and Diversity in Dialog Systems
We also present a novel data collection pipeline built atop our detection technique to automatically and iteratively mine unique data samples while discarding erroneous samples.
Rethinking Numerical Representations for Deep Neural Networks
With ever-increasing computational demand for deep learning, it is critical to investigate the implications of the numeric representation and precision of DNN model weights and activations on computational efficiency.
Data Collection for Dialogue System: A Startup Perspective
In this paper, we present a study of crowdsourcing methods for a user intent classification task in our deployed dialogue system.
